import json
import os
import unittest

from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase  ,     unittest.TestCase		):
   __lowerCAmelCase           =				GPTaTokenizer
   __lowerCAmelCase           =				GPTaTokenizerFast
   __lowerCAmelCase           =				True
   __lowerCAmelCase           =				{"""add_prefix_space""": True}
   __lowerCAmelCase           =				False







   def 						lowerCamelCase_	(     self						:						int				):





    """simple docstring"""


    super().setUp()

    # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
    UpperCamelCase			  =      [
        """l""",
        """o""",
        """w""",
        """e""",
        """r""",
        """s""",
        """t""",
        """i""",
        """d""",
        """n""",
        """\u0120""",
        """\u0120l""",
        """\u0120n""",
        """\u0120lo""",
        """\u0120low""",
        """er""",
        """\u0120lowest""",
        """\u0120newer""",
        """\u0120wider""",
        """<unk>""",
        """<|endoftext|>""",
    ]
    UpperCamelCase			  =      dict(zip(lowerCamelCase_   ,   range(len(lowerCamelCase_				)				)				)				)
    UpperCamelCase			  =      ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
    UpperCamelCase			  =      {"""unk_token""": """<unk>"""}

    UpperCamelCase			  =      os.path.join(self.tmpdirname   ,   VOCAB_FILES_NAMES["""vocab_file"""]				)
    UpperCamelCase			  =      os.path.join(self.tmpdirname   ,   VOCAB_FILES_NAMES["""merges_file"""]				)
    with open(self.vocab_file   ,   """w"""   ,   encoding="""utf-8"""				) as fp:
     fp.write(json.dumps(lowerCamelCase_				) + """\n"""				)
    with open(self.merges_file   ,   """w"""   ,   encoding="""utf-8"""				) as fp:
     fp.write("""\n""".join(lowerCamelCase_				)				)







   def 						lowerCamelCase_	(     self						:						int   ,   **lowerCamelCase_						:						int				):





    """simple docstring"""


    kwargs.update(self.special_tokens_map				)
    return GPTaTokenizer.from_pretrained(self.tmpdirname   ,   **lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						Dict   ,   **lowerCamelCase_						:						List[str]				):





    """simple docstring"""


    kwargs.update(self.special_tokens_map				)
    return GPTaTokenizerFast.from_pretrained(self.tmpdirname   ,   **lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						Any   ,   lowerCamelCase_						:						Any				):





    """simple docstring"""


    UpperCamelCase			  =      """lower newer"""
    UpperCamelCase			  =      """lower newer"""
    return input_text, output_text







   def 						lowerCamelCase_	(     self						:						str				):





    """simple docstring"""


    UpperCamelCase			  =      GPTaTokenizer(self.vocab_file   ,   self.merges_file   ,   **self.special_tokens_map				)
    UpperCamelCase			  =      """lower newer"""
    UpperCamelCase			  =      ["""\u0120low""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
    UpperCamelCase			  =      tokenizer.tokenize(lowerCamelCase_   ,   add_prefix_space=lowerCamelCase_				)
    self.assertListEqual(lowerCamelCase_   ,   lowerCamelCase_				)

    UpperCamelCase			  =      tokens + [tokenizer.unk_token]
    UpperCamelCase			  =      [14, 15, 10, 9, 3, 2, 15, 19]
    self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase_				)   ,   lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						List[str]				):





    """simple docstring"""


    if not self.test_rust_tokenizer:
     return

    UpperCamelCase			  =      self.get_tokenizer()
    UpperCamelCase			  =      self.get_rust_tokenizer(add_prefix_space=lowerCamelCase_				)

    UpperCamelCase			  =      """lower newer"""

    # Testing tokenization
    UpperCamelCase			  =      tokenizer.tokenize(lowerCamelCase_   ,   add_prefix_space=lowerCamelCase_				)
    UpperCamelCase			  =      rust_tokenizer.tokenize(lowerCamelCase_				)
    self.assertListEqual(lowerCamelCase_   ,   lowerCamelCase_				)

    # Testing conversion to ids without special tokens
    UpperCamelCase			  =      tokenizer.encode(lowerCamelCase_   ,   add_special_tokens=lowerCamelCase_   ,   add_prefix_space=lowerCamelCase_				)
    UpperCamelCase			  =      rust_tokenizer.encode(lowerCamelCase_   ,   add_special_tokens=lowerCamelCase_				)
    self.assertListEqual(lowerCamelCase_   ,   lowerCamelCase_				)

    # Testing conversion to ids with special tokens
    UpperCamelCase			  =      self.get_rust_tokenizer(add_prefix_space=lowerCamelCase_				)
    UpperCamelCase			  =      tokenizer.encode(lowerCamelCase_   ,   add_prefix_space=lowerCamelCase_				)
    UpperCamelCase			  =      rust_tokenizer.encode(lowerCamelCase_				)
    self.assertListEqual(lowerCamelCase_   ,   lowerCamelCase_				)

    # Testing the unknown token
    UpperCamelCase			  =      tokens + [rust_tokenizer.unk_token]
    UpperCamelCase			  =      [14, 15, 10, 9, 3, 2, 15, 19]
    self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(lowerCamelCase_				)   ,   lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						Any   ,   *lowerCamelCase_						:						Tuple   ,   **lowerCamelCase_						:						List[str]				):





    """simple docstring"""


    pass







   def 						lowerCamelCase_	(     self						:						List[Any]   ,   lowerCamelCase_						:						List[str]=15				):





    """simple docstring"""


    for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
     with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""				):
      UpperCamelCase			  =      self.rust_tokenizer_class.from_pretrained(lowerCamelCase_   ,   **lowerCamelCase_				)

      # Simple input
      UpperCamelCase			  =      """This is a simple input"""
      UpperCamelCase			  =      ["""This is a simple input 1""", """This is a simple input 2"""]
      UpperCamelCase			  =      ("""This is a simple input""", """This is a pair""")
      UpperCamelCase			  =      [
          ("""This is a simple input 1""", """This is a simple input 2"""),
          ("""This is a simple pair 1""", """This is a simple pair 2"""),
      ]

      # Simple input tests
      self.assertRaises(lowerCamelCase_   ,   tokenizer_r.encode   ,   lowerCamelCase_   ,   max_length=lowerCamelCase_   ,   padding="""max_length"""				)

      # Simple input
      self.assertRaises(lowerCamelCase_   ,   tokenizer_r.encode_plus   ,   lowerCamelCase_   ,   max_length=lowerCamelCase_   ,   padding="""max_length"""				)

      # Simple input
      self.assertRaises(
          lowerCamelCase_   ,   tokenizer_r.batch_encode_plus   ,   lowerCamelCase_   ,   max_length=lowerCamelCase_   ,   padding="""max_length"""   ,   )

      # Pair input
      self.assertRaises(lowerCamelCase_   ,   tokenizer_r.encode   ,   lowerCamelCase_   ,   max_length=lowerCamelCase_   ,   padding="""max_length"""				)

      # Pair input
      self.assertRaises(lowerCamelCase_   ,   tokenizer_r.encode_plus   ,   lowerCamelCase_   ,   max_length=lowerCamelCase_   ,   padding="""max_length"""				)

      # Pair input
      self.assertRaises(
          lowerCamelCase_   ,   tokenizer_r.batch_encode_plus   ,   lowerCamelCase_   ,   max_length=lowerCamelCase_   ,   padding="""max_length"""   ,   )







   def 						lowerCamelCase_	(     self						:						Dict				):





    """simple docstring"""


    UpperCamelCase			  =      GPTaTokenizer.from_pretrained(self.tmpdirname   ,   pad_token="""<pad>"""				)

    # Simple input
    UpperCamelCase			  =      """This is a simple input"""
    UpperCamelCase			  =      ["""This is a simple input looooooooong""", """This is a simple input"""]
    UpperCamelCase			  =      ("""This is a simple input""", """This is a pair""")
    UpperCamelCase			  =      [
        ("""This is a simple input loooooong""", """This is a simple input"""),
        ("""This is a simple pair loooooong""", """This is a simple pair"""),
    ]

    UpperCamelCase			  =      tokenizer.pad_token_id

    UpperCamelCase			  =      tokenizer(lowerCamelCase_   ,   padding="""max_length"""   ,   max_length=30   ,   return_tensors="""np"""				)
    UpperCamelCase			  =      tokenizer(lowerCamelCase_   ,   padding=lowerCamelCase_   ,   truncate=lowerCamelCase_   ,   return_tensors="""np"""				)
    UpperCamelCase			  =      tokenizer(*lowerCamelCase_   ,   padding="""max_length"""   ,   max_length=60   ,   return_tensors="""np"""				)
    UpperCamelCase			  =      tokenizer(lowerCamelCase_   ,   padding=lowerCamelCase_   ,   truncate=lowerCamelCase_   ,   return_tensors="""np"""				)

    # s
    # test single string max_length padding
    self.assertEqual(out_s["""input_ids"""].shape[-1]   ,   30				)
    self.assertTrue(pad_token_id in out_s["""input_ids"""]				)
    self.assertTrue(0 in out_s["""attention_mask"""]				)

    # s2
    # test automatic padding
    self.assertEqual(out_sa["""input_ids"""].shape[-1]   ,   33				)
    # long slice doesn't have padding
    self.assertFalse(pad_token_id in out_sa["""input_ids"""][0]				)
    self.assertFalse(0 in out_sa["""attention_mask"""][0]				)
    # short slice does have padding
    self.assertTrue(pad_token_id in out_sa["""input_ids"""][1]				)
    self.assertTrue(0 in out_sa["""attention_mask"""][1]				)

    # p
    # test single pair max_length padding
    self.assertEqual(out_p["""input_ids"""].shape[-1]   ,   60				)
    self.assertTrue(pad_token_id in out_p["""input_ids"""]				)
    self.assertTrue(0 in out_p["""attention_mask"""]				)

    # p2
    # test automatic padding pair
    self.assertEqual(out_pa["""input_ids"""].shape[-1]   ,   52				)
    # long slice pair doesn't have padding
    self.assertFalse(pad_token_id in out_pa["""input_ids"""][0]				)
    self.assertFalse(0 in out_pa["""attention_mask"""][0]				)
    # short slice pair does have padding
    self.assertTrue(pad_token_id in out_pa["""input_ids"""][1]				)
    self.assertTrue(0 in out_pa["""attention_mask"""][1]				)







   def 						lowerCamelCase_	(     self						:						Dict				):





    """simple docstring"""


    UpperCamelCase			  =      """$$$"""
    UpperCamelCase			  =      GPTaTokenizer.from_pretrained(self.tmpdirname   ,   bos_token=lowerCamelCase_   ,   add_bos_token=lowerCamelCase_				)

    UpperCamelCase			  =      """This is a simple input"""
    UpperCamelCase			  =      ["""This is a simple input 1""", """This is a simple input 2"""]

    UpperCamelCase			  =      tokenizer.bos_token_id

    UpperCamelCase			  =      tokenizer(lowerCamelCase_				)
    UpperCamelCase			  =      tokenizer(lowerCamelCase_				)

    self.assertEqual(out_s.input_ids[0]   ,   lowerCamelCase_				)
    self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids				)				)

    UpperCamelCase			  =      tokenizer.decode(out_s.input_ids				)
    UpperCamelCase			  =      tokenizer.batch_decode(out_sa.input_ids				)

    self.assertEqual(decode_s.split()[0]   ,   lowerCamelCase_				)
    self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa				)				)







   def 						lowerCamelCase_	(     self						:						Dict				):





    """simple docstring"""


    pass







   def 						lowerCamelCase_	(     self						:						Any				):





    """simple docstring"""


    UpperCamelCase			  =      [self.get_tokenizer(do_lower_case=lowerCamelCase_   ,   add_bos_token=lowerCamelCase_				)]
    for tokenizer in tokenizers:
     with self.subTest(f"""{tokenizer.__class__.__name__}"""				):
      UpperCamelCase			  =      """Encode this."""
      UpperCamelCase			  =      """This one too please."""
      UpperCamelCase			  =      tokenizer.encode(lowerCamelCase_   ,   add_special_tokens=lowerCamelCase_				)
      encoded_sequence += tokenizer.encode(lowerCamelCase_   ,   add_special_tokens=lowerCamelCase_				)
      UpperCamelCase			  =      tokenizer.encode_plus(
          lowerCamelCase_   ,   lowerCamelCase_   ,   add_special_tokens=lowerCamelCase_   ,   return_special_tokens_mask=lowerCamelCase_   ,   )
      UpperCamelCase			  =      encoded_sequence_dict["""input_ids"""]
      UpperCamelCase			  =      encoded_sequence_dict["""special_tokens_mask"""]
      self.assertEqual(len(lowerCamelCase_				)   ,   len(lowerCamelCase_				)				)

      UpperCamelCase			  =      [
          (x if not special_tokens_mask[i] else None) for i, x in enumerate(lowerCamelCase_				)
      ]
      UpperCamelCase			  =      [x for x in filtered_sequence if x is not None]
      self.assertEqual(lowerCamelCase_   ,   lowerCamelCase_				)





@require_tokenizers
class SCREAMING_SNAKE_CASE_		(      unittest.TestCase		):







   def 						lowerCamelCase_	(     self						:						str				):





    """simple docstring"""


    UpperCamelCase			  =      AutoTokenizer.from_pretrained("""facebook/opt-350m"""   ,   from_slow=lowerCamelCase_				)
    UpperCamelCase			  =      """A photo of a cat"""

    UpperCamelCase			  =      tokenizer.encode(
        lowerCamelCase_   ,   )
    self.assertEqual(lowerCamelCase_   ,   [2, 250, 1345, 9, 10, 4758]				)
    tokenizer.save_pretrained("""test_opt"""				)

    UpperCamelCase			  =      AutoTokenizer.from_pretrained("""./test_opt"""				)
    UpperCamelCase			  =      tokenizer.encode(
        lowerCamelCase_   ,   )
    self.assertEqual(lowerCamelCase_   ,   [2, 250, 1345, 9, 10, 4758]				)







   def 						lowerCamelCase_	(     self						:						List[Any]				):





    """simple docstring"""


    UpperCamelCase			  =      AutoTokenizer.from_pretrained("""facebook/opt-350m"""   ,   use_slow=lowerCamelCase_				)
    UpperCamelCase			  =      """A photo of a cat"""

    UpperCamelCase			  =      tokenizer.encode(
        lowerCamelCase_   ,   )
    # Same as above
    self.assertEqual(lowerCamelCase_   ,   [2, 250, 1345, 9, 10, 4758]				)







   @unittest.skip("""This test is failing because of a bug in the fast tokenizer"""				)
   def 						lowerCamelCase_	(     self						:						Any				):





    """simple docstring"""


    UpperCamelCase			  =      AutoTokenizer.from_pretrained("""facebook/opt-350m"""   ,   from_slow=lowerCamelCase_				)

    UpperCamelCase			  =      """bos"""
    UpperCamelCase			  =      tokenizer.get_vocab()["""bos"""]

    UpperCamelCase			  =      """A photo of a cat"""
    UpperCamelCase			  =      tokenizer.encode(
        lowerCamelCase_   ,   )
    # We changed the bos token
    self.assertEqual(lowerCamelCase_   ,   [3_1957, 250, 1345, 9, 10, 4758]				)
    tokenizer.save_pretrained("""./tok"""				)
    UpperCamelCase			  =      AutoTokenizer.from_pretrained("""./tok"""				)
    self.assertTrue(tokenizer.is_fast				)
    UpperCamelCase			  =      tokenizer.encode(
        lowerCamelCase_   ,   )
    self.assertEqual(lowerCamelCase_   ,   [3_1957, 250, 1345, 9, 10, 4758]				)

import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
     import os

     import jax.numpy as jnp
     from jax import jit

     from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
     from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

     _SCREAMING_SNAKE_CASE 		=      """0.12"""  # assumed parallelism: 8


if is_torch_available():
     import torch





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_=None       )		->	int:







 '''simple docstring'''
 if rng is None:
  UpperCamelCase			  =      random.Random()

 UpperCamelCase			  =      1
 for dim in shape:
  total_dims *= dim

 UpperCamelCase			  =      []
 for _ in range(UpperCamelCase_       ):
  values.append(rng.randint(0							,					vocab_size - 1       )       )

 UpperCamelCase			  =      np.array(UpperCamelCase_							,					dtype=jnp.intaa       ).reshape(UpperCamelCase_       )

 return output





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_=None       )		->	List[str]:







 '''simple docstring'''
 UpperCamelCase			  =      ids_tensor(UpperCamelCase_							,					vocab_size=2							,					rng=UpperCamelCase_       )
 # make sure that at least one token is attended to for each batch
 UpperCamelCase			  =      1
 return attn_mask
@require_flax
class SCREAMING_SNAKE_CASE_		:
   __lowerCAmelCase           =				None
   __lowerCAmelCase           =				()







   def 						lowerCamelCase_	(     self						:						Tuple				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase			  =      self.model_tester.prepare_config_and_inputs_for_common()

    # cut to half length & take max batch_size 3
    UpperCamelCase			  =      2
    UpperCamelCase			  =      inputs["""input_ids"""].shape[-1] // 2
    UpperCamelCase			  =      inputs["""input_ids"""][:max_batch_size, :sequence_length]

    UpperCamelCase			  =      jnp.ones_like(lowerCamelCase_				)
    UpperCamelCase			  =      attention_mask[:max_batch_size, :sequence_length]

    # generate max 5 tokens
    UpperCamelCase			  =      input_ids.shape[-1] + 5
    if config.eos_token_id is not None and config.pad_token_id is None:
     # hack to allow generate for models such as GPT2 as is done in `generate()`
     UpperCamelCase			  =      config.eos_token_id
    return config, input_ids, attention_mask, max_length







   @is_pt_flax_cross_test
   def 						lowerCamelCase_	(     self						:						Dict				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      self._get_input_ids_and_config()
    UpperCamelCase			  =      False
    UpperCamelCase			  =      max_length
    UpperCamelCase			  =      0

    for model_class in self.all_generative_model_classes:
     UpperCamelCase			  =      model_class(lowerCamelCase_				)

     UpperCamelCase			  =      model_class.__name__[4:]  # Skip the "Flax" at the beginning
     UpperCamelCase			  =      getattr(lowerCamelCase_   ,   lowerCamelCase_				)
     UpperCamelCase			  =      pt_model_class(lowerCamelCase_				).eval()
     UpperCamelCase			  =      load_flax_weights_in_pytorch_model(lowerCamelCase_   ,   flax_model.params				)

     UpperCamelCase			  =      flax_model.generate(lowerCamelCase_				).sequences
     UpperCamelCase			  =      pt_model.generate(torch.tensor(lowerCamelCase_   ,   dtype=torch.long				)				)

     if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
      UpperCamelCase			  =      flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

     self.assertListEqual(pt_generation_outputs.numpy().tolist()   ,   flax_generation_outputs.tolist()				)







   def 						lowerCamelCase_	(     self						:						List[str]				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      self._get_input_ids_and_config()
    UpperCamelCase			  =      False
    UpperCamelCase			  =      max_length

    for model_class in self.all_generative_model_classes:
     UpperCamelCase			  =      model_class(lowerCamelCase_				)

     UpperCamelCase			  =      model.generate(lowerCamelCase_				).sequences
     self.assertEqual(generation_outputs.shape[-1]   ,   lowerCamelCase_				)

     UpperCamelCase			  =      jit(model.generate				)
     UpperCamelCase			  =      jit_generate(lowerCamelCase_				).sequences

     self.assertListEqual(generation_outputs.tolist()   ,   jit_generation_outputs.tolist()				)







   def 						lowerCamelCase_	(     self						:						List[Any]				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      self._get_input_ids_and_config()
    UpperCamelCase			  =      True
    UpperCamelCase			  =      max_length

    for model_class in self.all_generative_model_classes:
     UpperCamelCase			  =      model_class(lowerCamelCase_				)

     UpperCamelCase			  =      model.generate(lowerCamelCase_				).sequences
     self.assertEqual(generation_outputs.shape[-1]   ,   lowerCamelCase_				)

     UpperCamelCase			  =      jit(model.generate				)
     UpperCamelCase			  =      jit_generate(lowerCamelCase_				).sequences

     self.assertListEqual(generation_outputs.tolist()   ,   jit_generation_outputs.tolist()				)







   def 						lowerCamelCase_	(     self						:						Any				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      self._get_input_ids_and_config()
    UpperCamelCase			  =      False
    UpperCamelCase			  =      max_length
    UpperCamelCase			  =      2

    for model_class in self.all_generative_model_classes:
     UpperCamelCase			  =      model_class(lowerCamelCase_				)

     UpperCamelCase			  =      model.generate(lowerCamelCase_				).sequences
     self.assertEqual(generation_outputs.shape[-1]   ,   lowerCamelCase_				)

     UpperCamelCase			  =      jit(model.generate				)
     UpperCamelCase			  =      jit_generate(lowerCamelCase_				).sequences

     self.assertListEqual(generation_outputs.tolist()   ,   jit_generation_outputs.tolist()				)







   def 						lowerCamelCase_	(     self						:						int				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      self._get_input_ids_and_config()
    UpperCamelCase			  =      False
    UpperCamelCase			  =      max_length
    UpperCamelCase			  =      2
    UpperCamelCase			  =      2

    for model_class in self.all_generative_model_classes:
     UpperCamelCase			  =      model_class(lowerCamelCase_				)

     UpperCamelCase			  =      model.generate(lowerCamelCase_				).sequences
     self.assertEqual(generation_outputs.shape[0]   ,   input_ids.shape[0] * config.num_return_sequences				)







   def 						lowerCamelCase_	(     self						:						Union[str, Any]				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      self._get_input_ids_and_config()
    UpperCamelCase			  =      True
    UpperCamelCase			  =      max_length
    UpperCamelCase			  =      0.8
    UpperCamelCase			  =      10
    UpperCamelCase			  =      0.3
    UpperCamelCase			  =      1
    UpperCamelCase			  =      8
    UpperCamelCase			  =      9

    for model_class in self.all_generative_model_classes:
     UpperCamelCase			  =      model_class(lowerCamelCase_				)

     UpperCamelCase			  =      model.generate(lowerCamelCase_				).sequences
     self.assertEqual(generation_outputs.shape[-1]   ,   lowerCamelCase_				)

     UpperCamelCase			  =      jit(model.generate				)
     UpperCamelCase			  =      jit_generate(lowerCamelCase_				).sequences

     self.assertListEqual(generation_outputs.tolist()   ,   jit_generation_outputs.tolist()				)







   def 						lowerCamelCase_	(     self						:						Optional[int]				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      self._get_input_ids_and_config()
    UpperCamelCase			  =      max_length
    UpperCamelCase			  =      1
    UpperCamelCase			  =      8
    UpperCamelCase			  =      9

    for model_class in self.all_generative_model_classes:
     UpperCamelCase			  =      model_class(lowerCamelCase_				)

     UpperCamelCase			  =      model.generate(lowerCamelCase_				).sequences
     self.assertEqual(generation_outputs.shape[-1]   ,   lowerCamelCase_				)

     UpperCamelCase			  =      jit(model.generate				)
     UpperCamelCase			  =      jit_generate(lowerCamelCase_				).sequences

     self.assertListEqual(generation_outputs.tolist()   ,   jit_generation_outputs.tolist()				)







   def 						lowerCamelCase_	(     self						:						Any				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      self._get_input_ids_and_config()
    UpperCamelCase			  =      max_length
    UpperCamelCase			  =      2
    UpperCamelCase			  =      1
    UpperCamelCase			  =      8
    UpperCamelCase			  =      9

    for model_class in self.all_generative_model_classes:
     UpperCamelCase			  =      model_class(lowerCamelCase_				)

     UpperCamelCase			  =      model.generate(lowerCamelCase_				).sequences
     self.assertEqual(generation_outputs.shape[-1]   ,   lowerCamelCase_				)

     UpperCamelCase			  =      jit(model.generate				)
     UpperCamelCase			  =      jit_generate(lowerCamelCase_				).sequences

     self.assertListEqual(generation_outputs.tolist()   ,   jit_generation_outputs.tolist()				)







   def 						lowerCamelCase_	(     self						:						int				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      self._get_input_ids_and_config()

    # pad attention mask on the left
    UpperCamelCase			  =      attention_mask.at[(0, 0)].set(0				)

    UpperCamelCase			  =      False
    UpperCamelCase			  =      max_length

    for model_class in self.all_generative_model_classes:
     UpperCamelCase			  =      model_class(lowerCamelCase_				)

     UpperCamelCase			  =      model.generate(lowerCamelCase_   ,   attention_mask=lowerCamelCase_				).sequences
     self.assertEqual(generation_outputs.shape[-1]   ,   lowerCamelCase_				)

     UpperCamelCase			  =      jit(model.generate				)
     UpperCamelCase			  =      jit_generate(lowerCamelCase_   ,   attention_mask=lowerCamelCase_				).sequences

     self.assertListEqual(generation_outputs.tolist()   ,   jit_generation_outputs.tolist()				)







   def 						lowerCamelCase_	(     self						:						Union[str, Any]				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      self._get_input_ids_and_config()

    # pad attention mask on the left
    UpperCamelCase			  =      attention_mask.at[(0, 0)].set(0				)

    UpperCamelCase			  =      True
    UpperCamelCase			  =      max_length

    for model_class in self.all_generative_model_classes:
     UpperCamelCase			  =      model_class(lowerCamelCase_				)

     UpperCamelCase			  =      model.generate(lowerCamelCase_   ,   attention_mask=lowerCamelCase_				).sequences
     self.assertEqual(generation_outputs.shape[-1]   ,   lowerCamelCase_				)

     UpperCamelCase			  =      jit(model.generate				)
     UpperCamelCase			  =      jit_generate(lowerCamelCase_   ,   attention_mask=lowerCamelCase_				).sequences

     self.assertListEqual(generation_outputs.tolist()   ,   jit_generation_outputs.tolist()				)







   def 						lowerCamelCase_	(     self						:						Any				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase  ,     UpperCamelCase			  =      self._get_input_ids_and_config()

    # pad attention mask on the left
    UpperCamelCase			  =      attention_mask.at[(0, 0)].set(0				)

    UpperCamelCase			  =      2
    UpperCamelCase			  =      max_length

    for model_class in self.all_generative_model_classes:
     UpperCamelCase			  =      model_class(lowerCamelCase_				)

     UpperCamelCase			  =      model.generate(lowerCamelCase_   ,   attention_mask=lowerCamelCase_				).sequences
     self.assertEqual(generation_outputs.shape[-1]   ,   lowerCamelCase_				)

     UpperCamelCase			  =      jit(model.generate				)
     UpperCamelCase			  =      jit_generate(lowerCamelCase_   ,   attention_mask=lowerCamelCase_				).sequences

     self.assertListEqual(generation_outputs.tolist()   ,   jit_generation_outputs.tolist()				)





@require_flax
class SCREAMING_SNAKE_CASE_		(      unittest.TestCase		):







   def 						lowerCamelCase_	(     self						:						Union[str, Any]				):





    """simple docstring"""


    UpperCamelCase			  =      AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-bert"""				)
    UpperCamelCase			  =      FlaxAutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-bert-flax-only"""				)

    UpperCamelCase			  =      """Hello world"""
    UpperCamelCase			  =      tokenizer(lowerCamelCase_   ,   return_tensors="""np"""				).input_ids

    # typos are quickly detected (the correct argument is `do_sample`)
    with self.assertRaisesRegex(lowerCamelCase_   ,   """do_samples"""				):
     model.generate(lowerCamelCase_   ,   do_samples=lowerCamelCase_				)

    # arbitrary arguments that will not be used anywhere are also not accepted
    with self.assertRaisesRegex(lowerCamelCase_   ,   """foo"""				):
     UpperCamelCase			  =      {"""foo""": """bar"""}
     model.generate(lowerCamelCase_   ,   **lowerCamelCase_				)
